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An Automation Support for Creating Configurable Process Models
 

An Automation Support for Creating Configurable Process Models

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This is an updated version of the work proposed in BIS 2011.

This is an updated version of the work proposed in BIS 2011.

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  • Actually, creating a configurable process model by merging manually a set of process models is a tedious, time consuming and error-prone task.From this comes a pressing need for providing an automation support for creating a configurable process model by merging a set of input process models.In this context, we aim to provide a merging algorithm that allows for automatically creating configurable process models.
  • The algorithm that that we propose should respect the following requirements:1 (read)This requirement means that if we have 3 input models, the resulting model should allow for all their behaviours. 2 (read)This requirement should help business analysts to find out where each element in the configurable model comes from, which will help for some configuration decisions. 3 (read)This requirement means that we are not merging the input models just for having a global model for all the possible behaviours but also for being confured in order to extract one of the input models.
  • Recall, we have identified three requirements for our algorithm.The first requirement is respected by considering identical identifiers or labels of the work nodes (events and functions).We do not remove or add any work node or arc. We remove only trivial connectors.The second requirement is fulfilled via annotations of the arcs and configurable connectors during the reduction step.The third requirement is respected via the introduction of explicit variation points: configurable connectors.
  • To evaluate our work, we have used 4 real world business processes from Dutch municipalities from the work of Gottshalk.These processes are : (read from slide)Each process have 5 variants which resulted into a total number of 20 process models originally available in a Protos annotation. We have manually translated and them into EPC. (They are available to anyone interested in them on simple request).
  • For each process, we have created a configurable process model as the merger of its input models.First we measured what we gained in size in terms of the number of the nodes at each situation. We observed that initially for the first process we had a total size of 190 node. Which is reduced into 131 node after creating the configurable model without reduction and we reached 71 node after the reduction. We can conclude that we reached a compression rate of around 50% after the reduction step. This is really important factor especially when companies are dealing with a huge number of process variants.We can also notice that the merging operation was performed in less than a second which is a great value in contrast to 130 man hour for merging 25% of an enterprise process models as reported by La Rosa.Complexity, (read from slide).

An Automation Support for Creating Configurable Process Models An Automation Support for Creating Configurable Process Models Presentation Transcript

  • Digital Enterprise Research Institute www.deri.ie An Automation Support for Creating Configurable Process Models WassimDerguech and Sami Bhiri WISE’11, Sydney, Australia, October 2011 Copyright 2011 Digital Enterprise Research Institute. All rights reserved. Enabling Networked Knowledge
  • Business Process VariantsDigital Enterprise Research Institute www.deri.ie International context: different process models are proposed for describing the same procedure. The main differences are due to local regulations and laws; modellers preferences; resource restrictions… The main challenges in such context is: how to manage these process variants in an efficient way. One possible solution: using configurable process models. Enabling Networked Knowledge 2
  • Configurable process modelDigital Enterprise Research Institute www.deri.ie … is a merger of multiple process variants that achieve the same goal in a given domain, which can be tailored for a particular setting, leading to ancustomized process model. Decide for a Decide Online Online travel for a Hotel Flight travel booking booking variation point Online Variant Phone Variant Decide Phone Online for a Hotel Flight Online Phone travel booking booking Hotel Hotel booking booking Decide Online Phone variation point for a Hotel Flight travel booking booking Phone Variant Online Variant Phone Online Flight Flight booking booking Enabling Networked Knowledge 3
  • C-EPCs: a notation for configurable process modelsDigital Enterprise Research Institute www.deri.ie Shipment is to be processed • Configurable connectors are the 1,2 Shipment variation points. processing 1,2 X X 2 Order Delivery Order generated and is to be generated and delivery opened created delivery opened 2 2,3 X X 1 2,3 Delivery Delivery 2,3 V V 2 2,3 Deliveries X X need to be planned 1,2,3 Freight Deliveries Delivery is Delivery packed need to be relevant for unblocked Delivery is Delivery planned shipment relevant for unblocked 1,2,3 shipment 1 1 2,3 V V X 1,2,3 Transportation Transportation 1,2,3 Shipment is Shipment is complete complete [Adapted from M. La Rosa 2010] Enabling Networked Knowledge 4
  • What we aim to do?Digital Enterprise Research Institute www.deri.ie Problem: Manual process model merging is tedious, time- consuming and error-prone. In this context we aim to provide a merging algorithm that allows for automatically creating configurable process models. Enabling Networked Knowledge 5
  • RequirementsDigital Enterprise Research Institute www.deri.ie1. The merged model should allow for the behavior of all the original models.2. Each element of the merged process model should be easily traced back to its original model.3. Business analysts should be able to derive one of the input models from the merged process model. [Adopted from M. La Rosa 2010] Enabling Networked Knowledge 6
  • Overview of the merge algorithmDigital Enterprise Research Institute www.deri.ie Order generated and delivery opened Shipment 1- Pre-process and merge is to be M2 processed Shipment Delivery M1 processing Delivery Order is to be generated and business process models created delivery opened Shipment X is to be processed Shipment Delivery processing Delivery is M3 V Delivery relevant for unblocked shipment Deliveries need to be X planned Delivery is Transportation Delivery relevant for M2 unblocked shipment Shipment is X complete Deliveries need to be planned Transportation Shipment is complete M1 Freight packed V Delivery is relevant for shipment Transportation Shipment is complete M3 2- Post-process the Shipment is to be processed configurable business process model 1,2 Shipment processing 1,2 X 2 Order Delivery generated and is to be delivery opened created 2 2,3 X 2 X 2 3 1 X 2,3 Delivery 2,3 X 3 2 V X 2 CM 2 Deliveries 1,2,3 need to be planned Freight Deliveries Delivery is Delivery packed need to be relevant for unblocked planned shipment 1,2,3 2,3 1 1 X X 1 V 3 3 2 X 2 1 X 3 2 X 1,2,3 Transportation Shipment is complete 1,2,3 CM 3- Reduce the configurable Shipment is to be processed 1,2 Shipment processing 1,2 business process model X 2 Order Delivery generated and is to be delivery opened created 2 2,3 X 2 X 2 3 1 X 2,3 Delivery 2,3 X 3 2 V X 2 2 Deliveries 1,2,3 need to be RC planned Freight Deliveries Delivery is Delivery packed need to be relevant for unblocked planned shipment 1,2,3 2,3 1 1 X X 1 V 3 3 2 M X 2 1 X 3 RCM 2 X 1,2,3 Transportation planning and processing 1,2 Shipment is complete Enabling Networked Knowledge 7
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 8
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 9
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 10
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 11
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 12
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 13
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 14
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 15
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 16
  • Digital Enterprise Research Institute www.deri.ie [Adapted from M. La Rosa 2010] Enabling Networked Knowledge 17
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 18
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 19
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 20
  • Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge 21
  • Back to the requirementsDigital Enterprise Research Institute www.deri.ie1. The merged model should allow for the behavior of all the original models.  By using identical identifiers/labels + no remove/add of work nodes + no remove of input arcs + remove only trivial connectors2. Each element of the merged process model should be easily traced back to its original model.  By using annotations3. Business analysts should be able to derive one of the input models from the merged process model.  By introducing variation points for configuration Enabling Networked Knowledge 11
  • EvaluationDigital Enterprise Research Institute www.deri.ie 4 real world business processes from Dutch municipalities [Gottshalk CAiSE09]:  Acknowledging an unborn child  Registering a newborn  Marriage  Issuing a death certificate Each process has 5 variants  5 x 4 = 20 models  Available in Protos1 modelling notation  translated manually into EPCProtos is part of Pallas Athenas BPM toolset BPM|one Enabling Networked Knowledge 12
  • EvaluationDigital Enterprise Research Institute www.deri.ie Output size Output size Execution Input size before after time (ms) reduction reduction P1 190 (29+56+52+29+24) 131 (31%) 71 (62%) 157 P2 347 (63+84+73+57+70) 276 (20%) 180 (48%) 235 P3 507 (76+127+127+114+63) 298 (41%) 214 (57%) 407 P4 355 (56+111+91+67+30) 266 (25%) 160 (54%) 282 Compression rate  Around 50% Execution Time  Great value in contrast to 130 man hour for merging 25% of an enterprise process models [M. La Rosa 2010]. Complexity O(|S|*|N|2) where |S| is the number of the input models and |N| is the total number of nodes of the largest model. Enabling Networked Knowledge 13
  • What in the future?Digital Enterprise Research Institute www.deri.ie1. Evaluation  Rich testbed: customer clearance processes  Formal verification, scalability...2. Extend the algorithm to:  Support approximate matching between labels  Partial ordering between tasks  Modularity3. Configuration  Semantic annotations to allow for automatically determine configuration options  User friendly configuration  Tracking configuration dependencies Enabling Networked Knowledge 14